Skip to main content

uncertainties

https://readthedocs.org/projects/uncertainties/badge/?version=latest https://img.shields.io/pypi/v/uncertainties.svg https://pepy.tech/badge/uncertainties/week https://codecov.io/gh/lmfit/uncertainties/branch/master/graph/badge.svg https://img.shields.io/github/actions/workflow/status/lmfit/uncertainties/python-package.yml?logo=github%20actions

The uncertainties package allows calculations with values that have uncertaintes, such as (2 +/- 0.1)*2 = 4 +/- 0.2. uncertainties takes the pain and complexity out of error propagation and calculations of values with uncertainties. For more information, see https://uncertainties.readthedocs.io/

Basic examples

>>> from uncertainties import ufloat
>>> x = ufloat(2, 0.25)
>>> x
2.0+/-0.25

>>> square = x**2
>>> square
4.0+/-1.0
>>> square.nominal_value
4.0
>>> square.std_dev  # Standard deviation
1.0

>>> square - x*x
0.0  # Exactly 0: correlations taken into account

>>> from uncertainties.umath import sin, cos  # and many more.
>>> sin(1+x**2)
-0.95892427466313845+/-0.2836621854632263

>>> print (2*x+1000).derivatives[x]  # Automatic calculation of derivatives
2.0

>>> from uncertainties import unumpy  # Array manipulation
>>> varr = unumpy.uarray([1, 2], [0.1, 0.2])
>>> print(varr)
[1.0+/-0.1 2.0+/-0.2]
>>> print(varr.mean())
1.50+/-0.11
>>> print(unumpy.cos(varr))
[0.540302305868+/-0.0841470984808 -0.416146836547+/-0.181859485365]

Main features

  • Transparent calculations with uncertainties: Little or no modification of existing code is needed to convert calculations of floats to calculations of values with uncertainties.

  • Correlations between expressions are correctly taken into account. Thus, x-x is exactly zero.

  • Most mathematical operations are supported, including most functions from the standard math module (sin,…). Comparison operators (>, ==, etc.) are supported too.

  • Many fast operations on arrays and matrices of numbers with uncertainties are supported.

  • Extensive support for printing numbers with uncertainties (including LaTeX support and pretty-printing).

  • Most uncertainty calculations are performed analytically.

  • This module also gives access to the derivatives of any mathematical expression (they are used by error propagation theory, and are thus automatically calculated by this module).

Installation or upgrade

To install uncertainties, use:

pip install uncertainties

To upgrade from an older version, use:

pip install --upgrade uncertainties

Further details are in the on-line documentation.

Git branches

The GitHub master branch is the latest development version, and is intended to be a stable pre-release version. It will be experimental, but should pass all tests.. Tagged releases will be available on GitHub, and correspond to the releases to PyPI. The GitHub gh-pages branch will contain a stable test version of the documentation that can be viewed at https://lmfit.github.io/uncertainties/. Other Github branches should be treated as unstable and in-progress development branches.

License

This package and its documentation are released under the Revised BSD License.

History

This package was created back around 2009 by Eric O. LEBIGOT.

Ownership of the package was taken over by the lmfit GitHub organization in 2024.

Metadata

Release files for uncertainties 3.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for uncertainties 3.2.3
File Size Uploaded Provenance
uncertainties-3.2.3.tar.gz 160.5 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for uncertainties 3.2.3
File Interpreter ABI Platform
uncertainties-3.2.3-py3-none-any.whl Python 3 none any Details

Total release size: 220.6 kB

Release files / uncertainties-3.2.3.tar.gz

Download URL uncertainties-3.2.3.tar.gz
Size 160.5 kB
Tags Source
SHA-256 checksum
How to use checksums
76a5653e686f617a42922d546a239e9efce72e6b35411b7750a1d12dcba03031
BLAKE2b-256 checksum
How to use checksums
fe0ccb09f33b26955399c675ab378e4063ed7e48422d3d49f96219ab0be5eba9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.8

Release files / uncertainties-3.2.3-py3-none-any.whl

Download URL uncertainties-3.2.3-py3-none-any.whl
Size 60.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
313353900d8f88b283c9bad81e7d2b2d3d4bcc330cbace35403faaed7e78890a
BLAKE2b-256 checksum
How to use checksums
8f5ef1e1dd319e35e962a4e00b33150a8868b6329cc1d19fd533436ba5488f09
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.8

Security for uncertainties 3.2.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

3.2.3 This release

2 release files No provenance

3.0

2.4.7

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page